Proportion Of Tweets Research Articles

Public Perceptions of Very Low Nicotine Content on Twitter: Observational Study.

Nicotine is a highly addictive agent in tobacco products. On June 21, 2022, the US Food and Drug Administration (FDA) announced a plan to propose a rule to establish a maximum nicotine level in cigarettes and other combusted tobacco products. This study aimed to understand public perception and discussion of very low nicotine content (VLNC) on Twitter (rebranded as X in July 2023). From December 12, 2021, to January 1, 2023, we collected Twitter data using relevant keywords such as "vln," "low nicotine," and "reduced nicotine." After a series of preprocessing steps (such as removing duplicates, retweets, and commercial tweets), we identified 3270 unique noncommercial tweets related to VLNC. We used an inductive method to assess the public perception and discussion of VLNC on Twitter. To establish a codebook, we randomly selected 300 tweets for hand-coding, including the attitudes (positive, neutral, and negative) toward VLNC (including its proposed rule) and major topics (13 topics). The Cohen κ statistic between the 2 human coders reached over 70%, indicating a substantial interrater agreement. The rest of the tweets were single-coded according to the codebook. We observed a significant peak in the discussion of VLNC on Twitter within 4 days of the FDA's announcement of the proposed rule on June 21, 2022. The proportion of tweets with a negative attitude toward VLNC was significantly lower than those with a positive attitude, 24.5% (801/3270) versus 37.09% (1213/3270) with P<.001 from the 2-proportion z test. Among tweets with a positive attitude, the topic "Reduce cigarette consumption or help smoking cessation" was dominant (1097/1213, 90.44%). Among tweets with a negative attitude, the topic "VLNC leads to more smoking" was the most popular topic (227/801, 28.34%), followed by "Similar toxicity of VLNC as a regular cigarette" (223/801, 27.84%), and "VLNC is not a good method for quitting smoking" (211/801, 26.34%). There is a more positive attitude toward VLNC than a negative attitude on Twitter, resulting from different opinions about VLNC. Discussions around VLNC mainly focused on whether VLNC could help people quit smoking.

Potential Impact of FDA Flavor Enforcement Policy on Vaping Behavior on Twitter.

In January 2020, the FDA announced an electronic cigarette (e-cigarette) flavor enforcement policy to restrict the sale of all unauthorized cartridge-based flavored e-cigarettes except tobacco and menthol flavors, which was implemented on 6 February 2020. This study aimed to understand the potential influence of this policy on one vaping behavior change—quitting vaping—using Twitter data. Twitter posts (tweets) related to e-cigarettes were collected between June 2019 and October 2020 through a Twitter streaming API. Based on the geolocation and keywords related to quitting vaping, tweets mentioning quitting vaping from the US were filtered. The demographics (age and gender) of Twitter users who mentioned quitting vaping were further inferred using a deep learning algorithm (deepFace). The proportion of tweets and Twitter users mentioning quitting vaping were compared between before and after the announcement and implementation of the flavor policy. Compared to before the FDA flavor policy, the proportion of tweets (from 0.11% to 0.20% and 0.24%) and Twitter users (from 0.15% to 0.70% and 0.86%) mentioning quitting vaping were significantly higher after the announcement and implementation of the policy (p-value < 0.001). In addition, there was an increasing trend in the proportion of female and young adults (18–35 years old) mentioning quitting vaping on Twitter after the announcement and implementation of the policy compared to that before the policy. Our results showed that the FDA flavor enforcement policy did have a positive impact on quitting vaping on Twitter. Our study provides an initial evaluation of the potential influence of the FDA flavor enforcement policy on user vaping behavior.

The Effect of Twitter App Policy Changes on the Sharing of Spatial Information through Twitter Users

Social media data have been widely used to gain insight into human mobility and activity patterns. Despite their abundance, social media data come with various data biases, such as user selection bias. In addition, a change in the Twitter app functionality may further affect the type of information shared through tweets and hence influence conclusions drawn from the analysis of such data. This study analyzes the effect of three Twitter app policy changes in 2015, 2017, and 2019 on the tweeting behavior of users, using part of London as the study area. The policy changes reviewed relate to a function allowing to attach exact coordinates to tweets by default (2015), the maximum allowable length of tweet posts (2017), and the limitation of sharing exact coordinates to the Twitter photo app (2019). The change in spatial aspects of users’ tweeting behavior caused by changes in user policy and Twitter app functionality, respectively, is quantified through measurement and comparison of six aspects of tweeting behavior between one month before and one month after the respective policy changes, which are: proportion of tweets with exact coordinates, tweet length, the number of placename mentions in tweet text and hashtags per tweet, the proportion of tweets with images among tweets with exact coordinates, and radius of gyration of tweeting locations. The results show, among others, that policy changes in 2015 and 2019 led users to post a smaller proportion of tweets with exact coordinates and that doubling the limit of allowable characters as part of the 2017 policy change increased the number of place names mentioned in tweets. The findings suggest that policy changes lead to a change in user contribution behavior and, in consequence, in the spatial information that can be extracted from tweets. The systematic change in user contribution behavior associated with policy changes should be specifically taken into consideration if jointly analyzing tweets from periods before and after such a policy change.

Spatial autocorrelation between human responses and Winter storm Grayson

AbstractThis study explores the relationship between Winter storm Grayson and human responses in the US by considering a multitude of periods, regions, socio‐demographic variables and spatial autocorrelation for the number of tweets. This study suggests that the proportion of tweets is highly concentrated in the north‐east region, which is the most damaged region in the winter storm week. Second, there is significant spatial autocorrelation for the number of tweets related to the winter storm. The lag coefficient values of the spatial lag model and the spatial error model are 0.362 and 0.437 at a 0.01 significance level, respectively. Third, younger people and people who have less than a high school degree show a positive coefficient value, whereas males, people with a high school degree, and those who have high income show a negative coefficient value for the number of tweets. This study shows that spatial regression models would be better models to understand human responses to natural disasters than the ordinary least squares model.

Using Social Media to Predict Food Deserts in the United States: Infodemiology Study of Tweets

BackgroundThe issue of food insecurity is becoming increasingly important to public health practitioners because of the adverse health outcomes and underlying racial disparities associated with insufficient access to healthy foods. Prior research has used data sources such as surveys, geographic information systems, and food store assessments to identify regions classified as food deserts but perhaps the individuals in these regions unknowingly provide their own accounts of food consumption and food insecurity through social media. Social media data have proved useful in answering questions related to public health; therefore, these data are a rich source for identifying food deserts in the United States.ObjectiveThe aim of this study was to develop, from geotagged Twitter data, a predictive model for the identification of food deserts in the United States using the linguistic constructs found in food-related tweets.MethodsTwitter’s streaming application programming interface was used to collect a random 1% sample of public geolocated tweets across 25 major cities from March 2020 to December 2020. A total of 60,174 geolocated food-related tweets were collected across the 25 cities. Each geolocated tweet was mapped to its respective census tract using point-to-polygon mapping, which allowed us to develop census tract–level features derived from the linguistic constructs found in food-related tweets, such as tweet sentiment and average nutritional value of foods mentioned in the tweets. These features were then used to examine the associations between food desert status and the food ingestion language and sentiment of tweets in a census tract and to determine whether food-related tweets can be used to infer census tract–level food desert status.ResultsWe found associations between a census tract being classified as a food desert and an increase in the number of tweets in a census tract that mentioned unhealthy foods (P=.03), including foods high in cholesterol (P=.02) or low in key nutrients such as potassium (P=.01). We also found an association between a census tract being classified as a food desert and an increase in the proportion of tweets that mentioned healthy foods (P=.03) and fast-food restaurants (P=.01) with positive sentiment. In addition, we found that including food ingestion language derived from tweets in classification models that predict food desert status improves model performance compared with baseline models that only include socioeconomic characteristics.ConclusionsSocial media data have been increasingly used to answer questions related to health and well-being. Using Twitter data, we found that food-related tweets can be used to develop models for predicting census tract food desert status with high accuracy and improve over baseline models. Food ingestion language found in tweets, such as census tract–level measures of food sentiment and healthiness, are associated with census tract–level food desert status.

Harnessing Twitter data to survey public attention and attitudes towards COVID-19 vaccines in the UK

Attitudes to COVID-19 vaccination vary considerably within and between countries. Although the contribution of socio-demographic factors to these attitudes has been studied, the role of social media and how it interacts with news about vaccine development and efficacy is uncertain. We examined around 2 million tweets from 522,893 persons in the UK from November 2020 to January 2021 to evaluate links between Twitter content about vaccines and major scientific news announcements about vaccines. The proportion of tweets with negative vaccine content varied, with reductions of 20–24% on the same day as major news announcement. However, the proportion of negative tweets reverted back to an average of around 40% within a few days. Engagement rates were higher for negative tweets. Public health messaging could consider the dynamics of Twitter-related traffic and the potential contribution of more targeted social media campaigns to address vaccine hesitancy.

Comprehensive Analyses of Twitter for Hurricane Irma

This study sheds new light on the sentiment analysis of Twitter for Hurricane Irma with respect to the US states and different periods based on comprehensive analyses. This study finds that tweets are highly related to Florida since the state is the most damaged state with respect to Hurricane Irma. This study also finds that people in other regions posted numerous tweets regarding praying for the people who are damaged by the hurricane and trying to assist in some disaster relief. Next, this study shows that the proportion of tweets is differentiated by periods and regions. In the pre-hurricane period, tweets are heavily concentrated in the South region. In the hurricane period, tweets are highly located in the Southeast and the Northeast regions. In the post-hurricane period, tweets are more posted in the Northeast region. Lastly, this study highlights that people upload numerous tweets including keywords related to regions, such as “Florida”, “Miami”, and keywords related to the hurricane, such as “safe”, “prepare”, and “evacuate”. People are also highly interested in the US president’s action to cope with the Hurricane Irma.

The social amplification and attenuation of COVID-19 risk perception shaping mask wearing behavior: A longitudinal twitter analysis.

Twitter represents a mainstream news source for the American public, offering a valuable vehicle for learning how citizens make sense of pandemic health threats like Covid-19. Masking as a risk mitigation measure became controversial in the US. The social amplification risk framework offers insight into how a risk event interacts with psychological, social, institutional, and cultural communication processes to shape Covid-19 risk perception. Qualitative content analysis was conducted on 7,024 mask tweets reflecting 6,286 users between January 24 and July 7, 2020, to identify how citizens expressed Covid-19 risk perception over time. Descriptive statistics were computed for (a) proportion of tweets using hyperlinks, (b) mentions, (c) hashtags, (d) questions, and (e) location. Six themes emerged regarding how mask tweets amplified and attenuated Covid-19 risk: (a) severity perceptions (18.0%) steadily increased across 5 months; (b) mask effectiveness debates (10.7%) persisted; (c) who is at risk (26.4%) peaked in April and May 2020; (d) mask guidelines (15.6%) peaked April 3, 2020, with federal guidelines; (e) political legitimizing of Covid-19 risk (18.3%) steadily increased; and (f) mask behavior of others (31.6%) composed the largest discussion category and increased over time. Of tweets, 45% contained a hyperlink, 40% contained mentions, 33% contained hashtags, and 16.5% were expressed as a question. Users ascribed many meanings to mask wearing in the social media information environment revealing that COVID-19 risk was expressed in a more expanded range than objective risk. The simultaneous amplification and attenuation of COVID-19 risk perception on social media complicates public health messaging about mask wearing.

Perceptions of the IQOS Heated Tobacco Product on Twitter in the United States

After the approval of the sales of IQOS in the United States market, discussions about IQOS have become active on social media. Twitter is a popular social media platform to understand public opinions toward IQOS. This study aims to explore public perceptions toward IQOS on Twitter in the United States. IQOS-related tweets from the United States between November 19, 2019, and August 24, 2020, were collected using a Twitter streaming application programming interface (API). Sentiment analysis was performed to determine whether the public perceptions toward IQOS were positive, neutral, or negative. In addition, topics discussed in these tweets were manually coded. From November 2019 to August 2020, the number of tweets discussing IQOS was relatively constant except for a peak starting from July 7, 2020, which lasted for 4 days. Among IQOS tweets with positive sentiments, the most popular topic is “IQOS is safer than cigarettes,” followed by “IQOS helps quit smoking.” Among tweets with negative sentiments, the most popular topic is “illegal marketing/selling to youth,” followed by “health risks/fire hazards.” “FDA approval/regulation” is the most popular topic for tweets with neutral sentiments. After the announcement of the United States Food and Drug Administration (FDA) enforcement policy on unauthorized flavored e-cigarette products on January 2, 2020, the proportion of tweets with positive attitudes toward IQOS significantly increased, while the proportion of negative tweets significantly decreased. Our study showed that the public perception of IQOS in the United States became more positive after the FDA enforcement policy on flavored e-cigarettes. While many Twitter users thought IQOS is safer than cigarettes and helps quit smoking, some Twitter users complained about the illegal marketing and health risks of IQOS. These findings provide useful information on future tobacco regulations.

Twitter Analysis of the Nonmedical Use and Side Effects of Methylphenidate: Machine Learning Study.

BackgroundMethylphenidate, a stimulant used to treat attention deficit hyperactivity disorder, has the potential to be used nonmedically, such as for studying and recreation. In an era when many people actively use social networking services, experience with the nonmedical use or side effects of methylphenidate might be shared on Twitter.ObjectiveThe purpose of this study was to analyze tweets about the nonmedical use and side effects of methylphenidate using a machine learning approach.MethodsA total of 34,293 tweets mentioning methylphenidate from August 2018 to July 2019 were collected using searches for “methylphenidate” and its brand names. Tweets in a randomly selected training dataset (6860/34,293, 20.00%) were annotated as positive or negative for two dependent variables: nonmedical use and side effects. Features such as personal noun, nonmedical use terms, medical use terms, side effect terms, sentiment scores, and the presence of a URL were generated for supervised learning. Using the labeled training dataset and features, support vector machine (SVM) classifiers were built and the performance was evaluated using F1 scores. The classifiers were applied to the test dataset to determine the number of tweets about nonmedical use and side effects.ResultsOf the 6860 tweets in the training dataset, 5.19% (356/6860) and 5.52% (379/6860) were about nonmedical use and side effects, respectively. Performance of SVM classifiers for nonmedical use and side effects, expressed as F1 scores, were 0.547 (precision: 0.926, recall: 0.388, and accuracy: 0.967) and 0.733 (precision: 0.920, recall: 0.609, and accuracy: 0.976), respectively. In the test dataset, the SVM classifiers identified 361 tweets (1.32%) about nonmedical use and 519 tweets (1.89%) about side effects. The proportion of tweets about nonmedical use was highest in May 2019 (46/2624, 1.75%) and December 2018 (36/2041, 1.76%).ConclusionsThe SVM classifiers that were built in this study were highly precise and accurate and will help to automatically identify the nonmedical use and side effects of methylphenidate using Twitter.

Stigma: the representation of mental health in UK newspaper Twitter feeds

Background The press’ representation of mental illness often includes images of people as dangerous, and there is evidence that this contributes to stigmatising understandings about mental illness. Little is known about how newspapers portray mental health on their Twitter feeds. Aim To explore the representation of mental health in the UK national press’ Twitter feeds. Method Content analysis was used to code the Tweets produced by UK national press in two time periods, 2014 and 2017. Chi-square analysis was used to identify trends. Results The analysis identified a significant reduction in the proportion of tweets that were characterised as Bad News between 2014 and 2017 (χ 2 = 14.476, d.f.=1, p < 0.001) and a significant increase in the tweets characterised as Understanding (χ 2 = 9.398, d.f.=1, p = 0.002). However, in 2017, 24% of the tweets were still characterised as Bad News. Readers did not retweet Bad News stories significantly more frequently than they were produced. Conclusions There is a positive direction of travel in the representations of mental health in the Twitter feeds of the UK press, but the level of Bad News stories remains a concern.

Authoritarian communication on social media: The relationship between democracy and leaders’ digital communicative practices

This research considers authoritarian leaders’ communicative practices on Twitter relative to their democratic counterparts. After a quantitative analysis of 144 world leaders’ Twitter accounts, this study identifies a positive and statistically significant relationship between a country’s level of democracy and its leader’s (1) average number of tweets per day and (2) proportion of tweets that are replies to other users. Additionally, qualitative case studies of Russia, Turkey, and Estonia reveal that authoritarian leaders’ accounts are of a relatively lower quality. Namely, they follow a less diverse set of accounts, have a higher proportion of inactive followers, and tend to tweet projections of power over policy statements. Ultimately, these results reveal an authoritarian preference for uni-directional communication and a democratic preference for multi-directional communication—a stylistic difference partially attributable to stronger incentives from political power structures in general and competitive elections in particular in democratic regimes.

#Proana: Pro-Eating Disorder Socialization on Twitter

PurposePro–eating disorder (ED) online movements support engagement with ED lifestyles and are associated with negative health consequences for adolescents with EDs. Twitter is a popular social media site among adolescents that provides a unique setting for Pro-ED content to be publicly exchanged. The purpose of this study was to investigate Pro-ED Twitter profiles' references to EDs and how their social connections (followers) reference EDs. MethodsA purposeful sample of 45 Pro-ED profiles was selected from Twitter. Profile information, all tweets, and a random sample of 100 of their followers' profile information were collected for content analysis using the Twitter Application Programming Interface. A codebook based on ED screening guidelines was applied to evaluate ED references. For each Pro-ED profile, proportion of tweets with ED references and proportion of followers with ED references in their own profile were evaluated. ResultsIn total, our 45 Pro-ED profiles generated 4,245 tweets for analysis. A median of 36.4% of profiles' tweets contained ED references. Pro-ED profiles had a median of 173 followers, and a median of 44.5% of followers had ED references. Pro-ED profiles with more tweets with ED references also tended to have more followers with ED references (β = .37, p < .01). ConclusionsFindings suggest that profiles which self-identify as Pro-ED express disordered eating patterns through tweets and have an audience of followers, many of whom also reference ED in their own profiles. ED socialization on Twitter might provide social support, but in the Pro-ED context this activity might also reinforce an ED identity.

The painful tweet: text, sentiment, and community structure analyses of tweets pertaining to pain.

BackgroundDespite the widespread popularity of social media, little is known about the extent or context of pain-related posts by users of those media.ObjectiveThe aim was to examine the type, context, and dissemination of pain-related tweets.MethodsWe used content analysis of pain-related tweets from 50 cities to unobtrusively explore the meanings and patterns of communications about pain. Content was examined by location and time of day, as well as within the context of online social networks.ResultsThe most common terms published in conjunction with the term “pain” included feel (n=1504), don’t (n=702), and love (n=649). The proportion of tweets with positive sentiment ranged from 13% in Manila to 56% in Los Angeles, CA, with a median of 29% across cities. Temporally, the proportion of tweets with positive sentiment ranged from 24% at 1600 to 38% at 2400, with a median of 32%. The Twitter-based social networks pertaining to pain exhibited greater sparsity and lower connectedness than did those social networks pertaining to common terms such as apple, Manchester United, and Obama. The number of word clusters in proportion to node count was greater for emotion terms such as tired (0.45), happy (0.43), and sad (0.4) when compared with objective terms such as apple (0.26), Manchester United (0.14), and Obama (0.25).ConclusionsTaken together, our results suggest that pain-related tweets carry special characteristics reflecting unique content and their communication among tweeters. Further work will explore how geopolitical events and seasonal changes affect tweeters’ perceptions of pain and how such perceptions may affect therapies for pain.